{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 食物和热量\n",
    "\n",
    "已知，以下食物每100g的千卡路里如下：\n",
    "\n",
    "小米粥 45  \n",
    "粗粮馒头 223  \n",
    "全麦面包 235  \n",
    "瘦猪肉 143  \n",
    "鸡翅 194  \n",
    "培根 181  \n",
    "火腿肠 212 \n",
    "\n",
    "建立一个空的字典，把上述键值对依此添加进去。 \n",
    "通过循环形式，显示，如“我吃了二两”+“小米粥”，“增加了”+“45”千卡路里。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "python_words = {'小米粥': '45',\n",
    "                '粗粮馒头': '223',\n",
    "                '全麦面包': '235',\n",
    "                '瘦猪肉':'143',\n",
    "                '鸡翅':'194',\n",
    "                '培根':'181',\n",
    "                '火腿肠':'212'\n",
    "                }\n",
    "for word, meaning in python_words.items():\n",
    "    print('\\n我吃了二两%s'%word + '增加了%s'%meaning+'千卡路里' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 运动和消耗\n",
    "常见三种运动的消耗热量表：  \n",
    "慢走 (一小时4公里) 255 卡  \n",
    "慢跑 (一小时9公里) 655 卡  \n",
    "羽毛球（一小时） 440 卡  \n",
    "\n",
    "模仿课件3-dict_set中的cell[9]做个刷题，直到答对而且答完为止。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "255卡\n",
      "\n",
      "What sport do you think this is?\n",
      "慢走 (一小时4公里)  慢跑 (一小时9公里)  羽毛球（一小时）  \n"
     ]
    }
   ],
   "source": [
    "python_words = {'慢走 (一小时4公里)': '255卡',\n",
    "                '慢跑 (一小时9公里)': '655',\n",
    "                '羽毛球（一小时）': '440卡',\n",
    "                }\n",
    "def show_words(python_words): \n",
    "    display_message = \"\"\n",
    "    for word in python_words.keys():\n",
    "        display_message += word + '  '\n",
    "    print(display_message)\n",
    "\n",
    "for meaning in python_words.values():\n",
    "    print(\"\\n%s\" % meaning)\n",
    "    \n",
    "    correct = False\n",
    "    while not correct:\n",
    "        \n",
    "        print(\"\\nWhat sport do you think this is?\")\n",
    "        show_words(python_words)\n",
    "        guessed_word = input(\"- \")    \n",
    "   \n",
    "        if python_words[guessed_word] == meaning:\n",
    "            print(\"You got it!\")\n",
    "            correct = True\n",
    "        else:\n",
    "            print(\"Sorry, that's just not the right sport.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary: A collection of key-value pairs.\n",
      "Function: A named set of instructions that defines a set of actions in Python.\n",
      "List: A collection of values that are not connected, but have an order.\n"
     ]
    }
   ],
   "source": [
    "# 对dict来说，输出时不按任何顺序\n",
    "# 可以通过sorted()函数使结果有序输出\n",
    "python_words = {'list': 'A collection of values that are not connected, but have an order.',\n",
    "                'dictionary': 'A collection of key-value pairs.',\n",
    "                'function': 'A named set of instructions that defines a set of actions in Python.',\n",
    "                }\n",
    "\n",
    "for word in sorted(python_words.keys()):\n",
    "    print(\"%s: %s\" % (word.title(), python_words[word]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 江西最高山\n",
    "\n",
    "[江西最高的十座山]{http://jx.ifeng.com/a/20160628/4693979_0.shtml}\n",
    "\n",
    "请模仿上例，创建包含山名和海拔的dict，并按如下形式循环输出：  \n",
    "“黄岗山海拔2157.8米”"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "军峰山海拔1760.9米\n",
      "\n",
      "九岭头海拔1794米\n",
      "\n",
      "玉京峰海拔1819.9米\n",
      "\n",
      "过风坳海拔1887米\n",
      "\n",
      "五虎岗海拔1891米\n",
      "\n",
      "武功山金顶(白鹤峰)海拔1918.3米\n",
      "\n",
      "齐云山海拔2061.3米\n",
      "\n",
      "南风面海拔2120.4米\n",
      "\n",
      "独竖尖海拔2128米\n",
      "\n",
      "黄岗山海拔2157.8米\n"
     ]
    }
   ],
   "source": [
    "mountain_altitude={'军峰山': '1760.9',\n",
    "                '九岭头': '1794',\n",
    "                '玉京峰': '1819.9',\n",
    "                '过风坳':'1887',\n",
    "                '五虎岗':'1891',\n",
    "                '武功山金顶(白鹤峰)':'1918.3',\n",
    "                '齐云山':'2061.3',\n",
    "                '南风面': '2120.4',\n",
    "                '独竖尖':'2128',\n",
    "                '黄岗山':'2157.8',}\n",
    "for mountain, altitude in mountain_altitude.items():\n",
    "    print('\\n%s'%mountain + '海拔%s'%altitude+'米' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 为室友打一波广告\n",
    "请参照课件3-dict_set中的cell[15]“卖室友”吧。  \n",
    "第一层dict的key可自选，如身高、爱好、技能等等。\n",
    "并以以下形式循环输出：  \n",
    "我知道的XXX，TA:  \n",
    "身高：160  \n",
    "爱好：吸猫  \n",
    "技能：python  \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "我知道的Lyl:\n",
      "身高: 155cm\n",
      "爱好: 吸猫\n",
      "技能: stata\n",
      "\n",
      "我知道的Ljh:\n",
      "身高: 165cm\n",
      "爱好: 跳舞\n",
      "技能: python\n",
      "\n",
      "我知道的Gj:\n",
      "身高: 160cm\n",
      "爱好: 王者荣耀\n",
      "技能: spss\n"
     ]
    }
   ],
   "source": [
    "my_roommates = {'lyl': {'身高': '155cm', '爱好': '吸猫', '技能':'stata'},\n",
    "                'ljh': {'身高': '165cm', '爱好': '跳舞', '技能':'python'},\n",
    "                'gj': {'身高': '160cm', '爱好': '王者荣耀', '技能':'spss'},\n",
    "        }\n",
    "for roommates_name, roommates_information in my_roommates.items():\n",
    "    print(\"\\n我知道的%s:\" % roommates_name.title())\n",
    "    print(\"身高: \" + roommates_information['身高'])\n",
    "    print(\"爱好: \" + roommates_information['爱好'])\n",
    "    print(\"技能: \" + roommates_information['技能'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "|"
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